What the data for this category looks like
The data for this category primarily comes from product help centers, customer organizational structures, training recording videos, and ticket systems. Daily documents include training slides, user manuals, training attendance sheets, and Q&A lists. The data update frequency is per order or per version. Most document structures are step-by-step operation instructions, permission configuration instructions, and FAQ entries. Some video recording data must first be transcribed to text before processing. Fields include operation step numbers, permission role names, and question classification tags. There are no unified fixed units, and some fields require supplementary information based on the customer’s business scenario.
What constraints these characteristics impose on the vector models and indexing workflow
Dispersed data sources lead to diverse text formats, so two types of input—plain text and transcribed text—must be supported. Unified preprocessing rules are required during indexing. Frequent update frequencies require the indexing process to support incremental updates, avoiding full reindexing which takes too long and delays timely synchronization of training documents. Most documents consist of step-by-step instructions and permission entries, with large differences in content granularity. Appropriate chunking rules must be matched to avoid semantic fragmentation and prevent breaking the contextual association of operation steps. Q&A data from ticket systems is mostly short text, so chunking parameters must be adjusted separately to meet the vectorization needs of short content. Customer organizational structure data involves permission roles, so field association information must be retained to avoid losing permission-related semantic associations during indexing.
Configuration Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
| Chunk Length | 800–1200 characters | Ensures semantic integrity for step-by-step operation and permission instruction documents in this category, avoiding overly fragmented or overly long single chunks |
| Overlap Length | 100–200 characters | Covers the contextual semantics of step-by-step operations, preventing chunking from breaking the associated logic of operation steps |
| Similarity Threshold | 0.72–0.85 | Meets the accuracy requirements for Q&A in this category, filtering out low-relevance permission instructions or operation step content |
| Single Search Limit | Top 5 entries | Controls the amount of returned information, avoiding excessive operation steps from interfering with user queries |
maxToken | 4096 token | Adapts to the context window limits of large models, ensuring integrated document content does not exceed the model’s processing range |
defaultToken | 256 token | Used to control the output length of a single recalled content, adapting to Q&A list data for short-form questions and answers |
Three Common Misconfigurations
- Phenomenon: A call failure error occurs when invoking a multimodal vector model, with a prompt indicating the specified model is not supported. Cause: The access key and API address for the corresponding multimodal model were not added in the system configuration, only the general text vector model was configured.
- Phenomenon: The number of search results after indexing does not match expectations, and some ticket Q&A data is not recalled. Cause: Chunking parameters were not adjusted for short-text Q&A lists, leading to short content being filtered out or not correctly chunked and stored in the index.
- Phenomenon: The association query tool cannot associate permission fields from customer organizational structure data. Cause: Field association information was not retained during indexing, only plain text content was vectorized, and the semantic association between roles and permissions was lost.
How to Verify Correct Configuration
- Upload a single user manual document, trigger manual indexing, and check the number of chunks in the indexing log to confirm that the chunk length and overlap length configurations are effective. This verification process is based on FastGPT v4.17.0.
- Initiate a query that includes operation steps, verify the relevance of the returned results to confirm that the similarity threshold configuration meets business requirements.
- Test the access interface for the multimodal vector model, check whether the returned results include vector data with corresponding characteristics to confirm that the model configuration is correct.
- Import permission data from customer organizational structures, use the association query tool, and confirm that the returned results include the association information between roles and permissions.
The values provided are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.